GLM 4.5V vs Ling 3.1 Flash
Ling 3.1 Flash leads GLM 4.5V on the Artificial Analysis Intelligence Index, 41.1 to 7.6, and Ling 3.1 Flash costs 8.0x less per output token ($0.225 vs $1.80 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
GLM 4.5V or Ling 3.1 Flash: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5V for work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 3
- GLM 4.5V only
- 38
- Ling 3.1 Flash only
- 1
- Like-for-like categories
- 3 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Ling 3.1 Flash
Ling 3.1 Flash leads, 41.1 against 7.6.
Same board, both measured
Long documents
Listed context window
Ling 3.1 Flash
Ling 3.1 Flash takes 262K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.1 Flash
GLM 4.5V $0.0015, Ling 3.1 Flash $0.0002 per turn.
List prices
At a glance
- Coding index
- GLM 4.5V not benchmarked, Ling 3.1 Flash not benchmarked.
- Widest category
- General, 83.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 4.5V 0.0% against Ling 3.1 Flash 83.0%, 83.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5V $1.80 against Ling 3.1 Flash $0.225 per million output tokens, 8.0x apart.
- Context window
- GLM 4.5V takes 66K tokens, Ling 3.1 Flash 262K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GLM 4.5V | Bars | Ling 3.1 Flash | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | GLM 4.5V | Ling 3.1 Flash | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 60.4% | Not measured | Only GLM 4.5V measured |
| SciCode | Not measured | 54.1% | Only Ling 3.1 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.8% | Not measured | Only GLM 4.5V measured |
| LMArena Text: coding | 1347 | Not measured | Only GLM 4.5V measured |
| LMArena Text: software and IT | 1353 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5V | Ling 3.1 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 41.1 | Ling 3.1 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.5V | Ling 3.1 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 22.5% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5V | Ling 3.1 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 68.4% | Not measured | Only GLM 4.5V measured |
| Humanity's Last Exam (Artificial Analysis run) | 6.3% | 39.4% | Ling 3.1 Flash |
| LMArena Text: hard prompts | 1335 | Not measured | Only GLM 4.5V measured |
| LMArena Text: expert | 1353 | Not measured | Only GLM 4.5V measured |
| LMArena Text: hard prompts in English | 1357 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: diagrams | 1151 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: homework | 1163 | Not measured | Only GLM 4.5V measured |
| Kagi LLM Benchmark | 59.8% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GLM 4.5V | Ling 3.1 Flash | Leader |
|---|---|---|---|
| AA Math Index | 73.0 | Not measured | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | 73.0% | Not measured | Only GLM 4.5V measured |
| LMArena Text: math | 1354 | Not measured | Only GLM 4.5V measured |
| LMArena Text: mathematical work | 1336 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.5V | Ling 3.1 Flash | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 78.8% | Not measured | Only GLM 4.5V measured |
| LMArena Text: science | 1330 | Not measured | Only GLM 4.5V measured |
| LMArena Text: medicine and healthcare | 1349 | Not measured | Only GLM 4.5V measured |
| LMArena Text: legal and government | 1338 | Not measured | Only GLM 4.5V measured |
| LMArena Text: business and finance | 1309 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5V | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5V | Ling 3.1 Flash | Leader |
|---|---|---|---|
| IFBench | 34.2% | Not measured | Only GLM 4.5V measured |
| Long context reasoning (AA-LCR) | 0.0% | 83.0% | Ling 3.1 Flash |
| LMArena Text | 1333 | Not measured | Only GLM 4.5V measured |
| LMArena Text: instruction following | 1311 | Not measured | Only GLM 4.5V measured |
| LMArena Text: creative writing | 1294 | Not measured | Only GLM 4.5V measured |
| LMArena Vision | 1154 | Not measured | Only GLM 4.5V measured |
| LMArena Text: English | 1362 | Not measured | Only GLM 4.5V measured |
| LMArena Text: non-English | 1303 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Chinese | 1337 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Spanish | 1336 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Russian | 1298 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Polish | 1302 | Not measured | Only GLM 4.5V measured |
| LMArena Text: multi-turn | 1333 | Not measured | Only GLM 4.5V measured |
| LMArena Text: longer queries | 1305 | Not measured | Only GLM 4.5V measured |
| LMArena Text: writing and language | 1302 | Not measured | Only GLM 4.5V measured |
| LMArena Text: entertainment and media | 1303 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: English | 1183 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: reading text | 1151 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 28 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5V | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Ling 3.1 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.1 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.1 Flash costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Z.ai GLM 4.5V | inclusionAI Ling 3.1 Flash |
|---|---|---|
| Maker | Z.ai | inclusionAI |
| Weights | Open weights | No public weights listed |
| Released | August 11, 2025 | October 2, 2026 |
| Context window | 66K | 262K |
| Input $/Mtok | $0.60 | $0.075 |
| Output $/Mtok | $1.80 | $0.225 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5V better than Ling 3.1 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5V for work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, GLM 4.5V or Ling 3.1 Flash?
GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Ling 3.1 Flash costs $0.075 and $0.225. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5V or Ling 3.1 Flash?
Ling 3.1 Flash, with 262K tokens against 66K.
How many benchmarks is the GLM 4.5V vs Ling 3.1 Flash comparison based on?
3 benchmarks have a result for both models as of October 22, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and Ling 3.1 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.